hdf5格式的matlab读写操作

2023-11-26 22:03| 来源: 网络整理| 查看: 265

最近要用caffe处理一个multi-label的回归问题，就是输出是一个向量，不是一个具体的数值，这个时候之前的leveldb格式就不凑效了，因为caffe源代码里面默认label是一个数值，网上搜了下，都说hdf5格式可以解决这个问题

在caffe里面，有一个hdf5的datalayer作为数据输入，从源代码来看，对于label的维数没做限制，剩下的问题就是如何生成hdf5的数据，目前只是找到了github上的一个人共享的用matlab写的hdf5数据的读写操作，在这我把代码粘贴出来

testHDF5.m

%% WRITING TO HDF5 filename='trial.h5'; num_total_samples=10000; % to simulate data being read from disk / generated etc. data_disk=rand(5,5,1,num_total_samples); label_disk=rand(10,num_total_samples); chunksz=100; created_flag=false; totalct=0; for batchno=1:num_total_samples/chunksz fprintf('batch no. %d\n', batchno); last_read=(batchno-1)*chunksz; % to simulate maximum data to be held in memory before dumping to hdf5 file batchdata=data_disk(:,:,1,last_read+1:last_read+chunksz); batchlabs=label_disk(:,last_read+1:last_read+chunksz); % store to hdf5 startloc=struct('dat',[1,1,1,totalct+1], 'lab', [1,totalct+1]); curr_dat_sz=store2hdf5(filename, batchdata, batchlabs, ~created_flag, startloc, chunksz); created_flag=true;% flag set so that file is created only once totalct=curr_dat_sz(end);% updated dataset size (#samples) end % display structure of the stored HDF5 file h5disp(filename); %% READING FROM HDF5 % Read data and labels for samples #1000 to 1999 data_rd=h5read(filename, '/data', [1 1 1 1000], [5, 5, 1, 1000]); label_rd=h5read(filename, '/label', [1 1000], [10, 1000]); fprintf('Testing ...\n'); try assert(isequal(data_rd, single(data_disk(:,:,:,1000:1999))), 'Data do not match'); assert(isequal(label_rd, single(label_disk(:,1000:1999))), 'Labels do not match'); fprintf('Success!\n'); catch err fprintf('Test failed ...\n'); getReport(err) end %delete(filename); % CREATE list.txt containing filename, to be used as source for HDF5_DATA_LAYER FILE=fopen('list.txt', 'w'); fprintf(FILE, '%s', filename); fclose(FILE); fprintf('HDF5 filename listed in %s \n', 'list.txt'); % NOTE: In net definition prototxt, use list.txt as input to HDF5_DATA as: % layers { % name: "data" % type: HDF5_DATA % top: "data" % top: "labelvec" % hdf5_data_param { % source: "/path/to/list.txt" % batch_size: 64 % } % }

store2hdf5.m

function [curr_dat_sz, curr_lab_sz] = store2hdf5(filename, data, labels, create, startloc, chunksz) % *data* is W*H*C*N matrix of images should be normalized (e.g. to lie between 0 and 1) beforehand % *label* is D*N matrix of labels (D labels per sample) % *create* [0/1] specifies whether to create file newly or to append to previously created file, useful to store information in batches when a dataset is too big to be held in memory (default: 1) % *startloc* (point at which to start writing data). By default, % if create=1 (create mode), startloc.data=[1 1 1 1], and startloc.lab=[1 1]; % if create=0 (append mode), startloc.data=[1 1 1 K+1], and startloc.lab = [1 K+1]; where K is the current number of samples stored in the HDF % chunksz (used only in create mode), specifies number of samples to be stored per chunk (see HDF5 documentation on chunking) for creating HDF5 files with unbounded maximum size - TLDR; higher chunk sizes allow faster read-write operations % verify that format is right dat_dims=size(data); lab_dims=size(labels); num_samples=dat_dims(end); assert(lab_dims(end)==num_samples, 'Number of samples should be matched between data and labels'); if ~exist('create','var') create=true; end if create %fprintf('Creating dataset with %d samples\n', num_samples); if ~exist('chunksz', 'var') chunksz=1000; end if exist(filename, 'file') fprintf('Warning: replacing existing file %s \n', filename); delete(filename); end h5create(filename, '/data', [dat_dims(1:end-1) Inf], 'Datatype', 'single', 'ChunkSize', [dat_dims(1:end-1) chunksz]); % width, height, channels, number h5create(filename, '/label', [lab_dims(1:end-1) Inf], 'Datatype', 'single', 'ChunkSize', [lab_dims(1:end-1) chunksz]); % width, height, channels, number if ~exist('startloc','var') startloc.dat=[ones(1,length(dat_dims)-1), 1]; startloc.lab=[ones(1,length(lab_dims)-1), 1]; end else % append mode if ~exist('startloc','var') info=h5info(filename); prev_dat_sz=info.Datasets(1).Dataspace.Size; prev_lab_sz=info.Datasets(2).Dataspace.Size; assert(prev_dat_sz(1:end-1)==dat_dims(1:end-1), 'Data dimensions must match existing dimensions in dataset'); assert(prev_lab_sz(1:end-1)==lab_dims(1:end-1), 'Label dimensions must match existing dimensions in dataset'); startloc.dat=[ones(1,length(dat_dims)-1), prev_dat_sz(end)+1]; startloc.lab=[ones(1,length(lab_dims)-1), prev_lab_sz(end)+1]; end end if ~isempty(data) h5write(filename, '/data', single(data), startloc.dat, size(data)); h5write(filename, '/label', single(labels), startloc.lab, size(labels)); end if nargout info=h5info(filename); curr_dat_sz=info.Datasets(1).Dataspace.Size; curr_lab_sz=info.Datasets(2).Dataspace.Size; end end

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